scholarly journals Simulating Protein Structure to Support Clinical Decisions: Predicting the Ligand Specific Impact of Genomic Variation on Opioid Binding

2020 ◽  
Author(s):  
Vikram Shivakumar ◽  
Whitney Reid ◽  
Subha Madhavan ◽  
Matthew D. McCoy
Keyword(s):  
Genetic Variation ◽  
Binding Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Opioid Receptor ◽  
Drug Dosage ◽  
Drug Binding ◽  
Docking Simulations ◽  
Simulation Results ◽  
The Impact

Abstract Background: Predicting the impact of missense protein variants on drug binding would have a widespread implications on the practice of genomic medicine, including matching a molecular therapy and dosage to an individual’s genome sequence. Genetic variation is widespread within G-protein-coupled receptors, which can affect overall structure and conformation of the receptors. These structural changes in turn impact ligand binding interactions, which may change the overall dosage requirements for target drugs. In this study, we used molecular docking simulations to explore the effect of missense variants on opioid drug binding affinity to the opioid receptor mu 1 (OPMR1). Methods: Using high-throughput, in silico docking simulations, the binding interactions of 27 opioid drugs to naturally occurring variants in opioid receptor mu 1 (OPRM1) were used to predict changes to ligand binding affinity. The binding energy of the small molecules to the wild-type receptor was compared to an experimentally derived inhibitory constant (Ki) for validation, and the variant-induced disruptions in variant:drug interactions used to predict the impact on the effective drug dosage. Results: The binding energies for each drug-variant receptor pair relative to the wildtype receptor and drug showed trends across drugs, with some variants showing enhancing (238I, 302I) or diminishing (235M, 235N) effects on binding affinity. The 153V variant showed increased binding affinity for certain drugs, and decreased affinity for others. The simulation results correlated well with experimentally derived inhibitory constants (R2 = 0.69), and an exponential regression model revealed how changes in relative binding energy between wildtype and variant structures predict changes to Ki.Conclusions: The simulation results illustrate the potential for integrating genetic variation into the process of development of small molecule therapies to support genomic-driven medicine. Depending on the drug and location, amino acid variation can either increase or decrease the strength of the molecular interactions and should be considered when determining drug dosage. The scale of variation and the cost of experimental characterization underscores the potential for accurate simulation based methods to inform clinical decisions.

scholarly journals Key genetic elements, single and in clusters, underlying geographically dependent SARS-CoV-2 genetic adaptation and their impact on binding affinity for drugs and immune control

2020 ◽  
Author(s):  
Romina Salpini ◽  
Mohammad Alkhatib ◽  
Giosuè Costa ◽  
Lorenzo Piermatteo ◽  
Francesca Alessandra Ambrosio ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Neutralizing Antibodies ◽  
Cell Epitope ◽  
Drug Binding ◽  
Genetic Adaptation ◽  
Close Monitoring ◽  
Purine Analogues ◽  
Genetic Elements ◽  
Evolutionary Diversification ◽  
The Impact

Abstract Objectives To define key genetic elements, single or in clusters, underlying SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) evolutionary diversification across continents, and their impact on drug-binding affinity and viral antigenicity. Methods A total of 12 150 SARS-CoV-2 sequences (publicly available) from 69 countries were analysed. Mutational clusters were assessed by hierarchical clustering. Structure-based virtual screening (SBVS) was used to select the best inhibitors of 3-chymotrypsin-like protease (3CL-Pr) and RNA-dependent RNA polymerase (RdRp) among the FDA-approved drugs and to evaluate the impact of mutations on binding affinity of these drugs. The impact of mutations on epitope recognition was predicted following Grifoni et al. (Cell Host Microbe 2020; 27 671–80.) Results Thirty-five key mutations were identified (prevalence: ≥0.5%), residing in different viral proteins. Sixteen out of 35 formed tight clusters involving multiple SARS-CoV-2 proteins, highlighting intergenic co-evolution. Some clusters (including D614GSpike + P323LRdRp + R203KN + G204RN) occurred in all continents, while others showed a geographically restricted circulation (T1198KPL-Pr + P13LN + A97VRdRp in Asia, L84SORF-8 + S197LN in Europe, Y541CHel + H504CHel + L84SORF-8 in America and Oceania). SBVS identified 20 best RdRp inhibitors and 21 best 3CL-Pr inhibitors belonging to different drug classes. Notably, mutations in RdRp or 3CL-Pr modulate, positively or negatively, the binding affinity of these drugs. Among them, P323LRdRp (prevalence: 61.9%) reduced the binding affinity of specific compounds including remdesivir while it increased the binding affinity of the purine analogues penciclovir and tenofovir, suggesting potential hypersusceptibility. Finally, specific mutations (including Y541CHel + H504CHel) strongly hampered recognition of Class I/II epitopes, while D614GSpike profoundly altered the structural stability of a recently identified B cell epitope target of neutralizing antibodies (amino acids 592–620). Conclusions Key genetic elements reflect geographically dependent SARS-CoV-2 genetic adaptation, and may play a potential role in modulating drug susceptibility and hampering viral antigenicity. Thus, a close monitoring of SARS-CoV-2 mutational patterns is crucial to ensure the effectiveness of treatments and vaccines worldwide.

Improving Ligand-Ranking of AutoDock Vina by Changing the Empirical Parameters

2021 ◽  
Author(s):  
T. Ngoc Han Pham ◽  
Trung Hai Nguyen ◽  
Nguyen Minh Tam ◽  
Thien Y Vu ◽  
Nhat Truong Pham ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Ligand Binding ◽  
Success Rate ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Autodock Vina ◽  
Docking Simulations ◽  
Low Correlation ◽  
Very High ◽  
Ligand Ranking

AutoDock Vina (Vina) achieved a very high docking-success rate, p ̂, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ̂ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R_set1=0.556±0.025 compared with R_Default=0.493±0.028 obtained by the original Vina and R_(Vina 1.2)=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (R_set1=0.621±0.016) than the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

scholarly journals Noncontiguous regions in the extracellular domain of EGF receptor define ligand-binding specificity.

1991 ◽  
Vol 2 (5) ◽  
pp. 337-345 ◽  
Author(s):  
I Lax ◽  
R Fischer ◽  
C Ng ◽  
J Segre ◽  
A Ullrich ◽  
...  
Keyword(s):  
Binding Energy ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Affinity ◽  
Egf Receptor ◽  
Binding Properties ◽  
Amino Terminal ◽  
High Affinity ◽  
Domain Iii ◽  
Domain I

Murine epidermal growth factor (EGF) binds with approximately 250-fold higher binding affinity to the human EGF receptor (EGFR) than to the chicken EGFR. This difference in binding affinity enabled the identification of a major ligand-binding domain for EGF by studying the binding properties of various chicken/human EGFR chimera expressed in transfected cells lacking endogenous EGFR. It was shown that domain III of EGFR is a major ligand-binding region. Here, we analyze the binding properties of novel chicken/human chimera to further delineate the contact sequences in domain III and to assess the role of other regions of EGFR for their contribution to the display of high-affinity EGF binding. The chimeric receptors include chicken EGFR containing domain I of the human EGFR, chicken receptor containing domain I and III of the human EGFR, and two chimeric chicken EGFR containing either the amino terminal or the carboxy terminal halves of domain III of human EGFR, respectively. In addition, the binding of various human-specific anti-EGFR monoclonal antibodies that interfere with EGF binding is also compared. It is concluded that noncontiguous regions of the EGFR contribute additively to the binding of EGF. Each of the two halves of domain III has a similar contribution to the binding energy, and the sum of both is close to that of the entire domain III. This suggests that the folding of domain III juxtaposes sequences that together constitute the ligand-binding site. Domain I also provides a contribution to the binding energy, and the added contributions of both domain I and III to the binding energy generate the high-affinity binding site typical of human EGFR.

μ Opioid receptor: role for the amino terminus as a determinant of ligand binding affinity

2000 ◽  
Vol 76 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Kirti Chaturvedi ◽  
Mandana Shahrestanifar ◽  
Richard D Howells
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Opioid Receptor ◽  
Amino Terminus ◽  
Μ Opioid Receptor

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

Distinct Modulation of Wild-Type and Selective Gene Mutated Vitamin D Receptor by Essential Poly Unsaturated Fatty Acids

2021 ◽  
Vol 21 ◽  
Author(s):  
Hari Balaji ◽  
Selvaraj Ayyamperuma ◽  
Niladri Saha ◽  
Shyam Sundar Pottabathula ◽  
Jubie Selvaraj ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Vitamin D ◽  
Ligand Binding ◽  
Vitamin D Deficiency ◽  
Binding Affinity ◽  
Unsaturated Fatty Acids ◽  
Binding Domain ◽  
Ligand Binding Domain ◽  
Binding Energies ◽  
Wild Type

: Vitamin-D deficiency is a global concern. Gene mutations in the vitamin D receptor’s (VDR) ligand binding domain (LBD) variously alter the ligand binding affinity, heterodimerization with retinoid X receptor (RXR) and inhibit coactivator interactions. These LBD mutations may result in partial or total hormone unresponsiveness. A plethora of evidence report that selective long chain polyunsaturated fatty acids (PUFAs) including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and arachidonic acid (AA) bind to the ligand-binding domain of VDR and lead to transcriptional activation. We therefore hypothesize that selective PUFAs would modulate the dynamics and kinetics of VDRs, irrespective bioactive of vitamin-D binding. The spatial arrangements of the selected PUFAs in VDR active site were examined by in-silico docking studies. The docking results revealed that PUFAs have fatty acid structure-specific binding affinity towards VDR. The calculated EPA, DHA & AA binding energies (Cdocker energy) were lesser compared to vitamin-D in wild type of VDR (PDB id: 2ZLC). Of note, the DHA has higher binding interactions to the mutated VDR (PDB id: 3VT7) when compared to the standard Vitamin-D. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding of DHA with mutated VDR complex. These findings suggest the unique roles of PUFAs in VDR activation and may offer alternate strategy to circumvent vitamin-D deficiency.

A New Series Of 1,3-Dimethylxanthine Based Adenosine A2a Receptor Antagonists As A Non-Dopaminergic Treatment Of Parkinson’s Disease

2020 ◽  
Vol 17 ◽  
Author(s):  
Suman Rohilla ◽  
Ranju Bansal ◽  
Puneet Chauhan ◽  
Sonja Kachler ◽  
Karl-Norbert Klotz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Molecular Docking ◽  
Binding Affinity ◽  
Docking Studies ◽  
Binding Modes ◽  
Molecular Docking Studies ◽  
In Silico Docking ◽  
Adenosine A2a Receptor Antagonists ◽  
The Impact

Background: Adenosine receptors (AR) have emerged as competent and innovative nondopaminergic targets for the development of potential drug candidates and thus constitute an effective and safer treatment approach for Parkinson’s disease (PD). Xanthine derivatives are considered as potential candidates for the treatment Parkinson’s disease due to their potent A2A AR antagonistic properties. Objective: The objectives of the work are to study the impact of substituting N7-position of 8-m/pchloropropoxyphenylxanthine structure on in vitro binding affinity of compounds with various AR subtypes, in vivo antiparkinsonian activity and binding modes of newly synthesized xanthines with A2A AR in molecular docking studies. Methods: Several new 7-substituted 8-m/p-chloropropoxyphenylxanthine analogues have been prepared. Adenosine receptor binding assays were performed to study the binding interactions with various subtypes and perphenazine induced rat catatonia model was used for antiparkinsonian activity. Molecular docking studies were performed using Schrödinger molecular modeling interface. Results: 8-para-substituted xanthine 9b bearing an N7-propyl substituent displayed the highest affinity towards A2A AR (Ki = 0.75 µM) with moderate selectivity versus other AR subtypes. 7-Propargyl analogue 9d produced significantly longlasting antiparkinsonian effects and also produced potent and selective binding affinity towards A2A AR. In silico docking studies further highlighted the crucial structural components required to develop xanthine derived potential A2A AR ligands as antiparkinsonian agents. Conclusion: A new series of 7-substituted 8-m/p-chloropropoxyphenylxanthines having good affinity for A2A AR and potent antiparkinsonian activity has been developed.

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